A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Engineering. Volume 21, Issue 4, pp. 465-478.
Ran Wolff, Kanishka Bhaduri, Hillol Kargupta: A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Trans. Knowl. Data Eng. 21(4): 465-478 (2009) 2008; 21 : Kanishka Bhaduri, Ran Wolff, Chris Giannella, Hillol Kargupta: Distributed Decision-Tree Induction in Peer-to-Peer Systems.
The field of Distributed Data Mining (DDM) deals with the problem of analyzing data by paying careful attention to the distributed computing, storage, communication, and human-factor related resources. ... A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems more. by Ran Wolff. ... A Local Facility Location Algorithm ...
In this thesis, we propose a data mining algorithm to mine association rules events from multiple data streams in an incremental manner. The scope of the proposed algorithm lies in identifying frequent associated events that can generate interesting association rules. We investigate a generic and an efficient stream mining approach to
(2009) A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Engineering 21 :4, 465-478. (2009) Local Construction of Near-Optimal Power Spanners for Wireless Ad Hoc Networks.
Data Stream Mining: A Review of Learning Methods and Frameworks Svitlana Volkova Center for Language and Speech Processing Johns Hopkins University [email protected] October 12, 2012 Abstract The goal of the paper is to review methods, algorithms and frameworks for processing and analyzing real time data streams.
The widespread deployment of digital systems has led to a large increase in the number of sources of streaming digital data such as text and transactional data, digital audio, video and image data, instant messages, network packet traces, and variety of sensor data. The Stream Processing Core is a distributed stream-mining middleware, built to ...
Distributed Systems Stream Groups Local Patterns Global Patterns Figure 1: Distributed data mining architecture. local patterns (details in section 5). 3) From the global patterns, each autonomous system further reﬁnes/veriﬁes their local patterns. There are two main options on where the global patterns are computed. First, all local patterns
Jan 18, 2012 In: Proceedings of the SIAM international conference on data mining (SDM06), pp 430–441. 69. Wolff R, Bhaduri K, Kargupta H (2009) A generic local algorithm for mining data streams in large distributed systems. IEEE Trans on Knowl Data Eng …
Nov 01, 2019 Algorithm 1 presents the systematic flow of steps in randomizing the data to produce a privacy-preserving output. The algorithm accepts input dataset (D), privacy budget ϵ (defined in Eq.(C.21)), window size (ws) and threshold (t) as the input parameters.The window size defines the number of data instances to be perturbed in one cycle of randomization.
Distributed data mining techniques have been thoroughly reviewed by Park and Kargupta in . On the other hand, the eld of data stream mining has been concisely reviewed in . More recently, the utilisation of smart phones’ sensing capabilities to be used to learn about the user’s activities have been explored in [13,9].
implementing distributed clustering algorithms is that it is possible that an algorithm will get stuck in local optima, never finding the optimal solution. Attempting to converge on an optimal solution can be even more difficult when data is distributed, where no single node is fully aware of all data points.
WavingSketch: An Unbiased and Generic Sketch for Finding Top-k Items in Data Streams. ACM SIGKDD 2020 (top #1 conference in Data Mining). PDF Download; Tong Yang, Junzhi Gong, Haowei Zhang, Lei Zou, Lei Shi and Xiaoming Li. HeavyGuardian: Separate and Guard Hot Items in Data Streams. SIGKDD 2018 (top #1 conference in Data Mining).
This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggrega - tion useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induc- tion, feature selection, and more.
In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters.
Jul 25, 2020 Mining association rules and frequent itemsets have a well established history and in fact [15, 20, 22] discuss algorithms to accomplish these tasks in the context of streaming data.The accumulative model, the sliding window model, and the weighted accumulative model are presented by Yu and Chi  as ways of handling streaming data.The accumulative model and weighted …
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